CVFeb 10, 2025

Early Operative Difficulty Assessment in Laparoscopic Cholecystectomy via Snapshot-Centric Video Analysis

arXiv:2502.07008v10.026 citationsh-index: 32Int J Comput Assist Radiol Surg
AI Analysis90

This work addresses the problem of operative difficulty assessment in laparoscopic cholecystectomy for surgeons and healthcare professionals, which can lead to improved surgical outcomes and operating room planning.

The authors tackled the problem of early operative difficulty assessment in laparoscopic cholecystectomy, achieving a 0.22 point improvement over baselines in correct predictions and at least 9 and 5 percentage points improvement in F1 score and top1-accuracy, respectively. Their model, SurgPrOD, demonstrated effectiveness in predicting operative difficulty.

Purpose: Laparoscopic cholecystectomy (LC) operative difficulty (LCOD) is highly variable and influences outcomes. Despite extensive LC studies in surgical workflow analysis, limited efforts explore LCOD using intraoperative video data. Early recognition of LCOD could allow prompt review by expert surgeons, enhance operating room (OR) planning, and improve surgical outcomes. Methods: We propose the clinical task of early LCOD assessment using limited video observations. We design SurgPrOD, a deep learning model to assess LCOD by analyzing features from global and local temporal resolutions (snapshots) of the observed LC video. Also, we propose a novel snapshot-centric attention (SCA) module, acting across snapshots, to enhance LCOD prediction. We introduce the CholeScore dataset, featuring video-level LCOD labels to validate our method. Results: We evaluate SurgPrOD on 3 LCOD assessment scales in the CholeScore dataset. On our new metric assessing early and stable correct predictions, SurgPrOD surpasses baselines by at least 0.22 points. SurgPrOD improves over baselines by at least 9 and 5 percentage points in F1 score and top1-accuracy, respectively, demonstrating its effectiveness in correct predictions. Conclusion: We propose a new task for early LCOD assessment and a novel model, SurgPrOD analyzing surgical video from global and local perspectives. Our results on the CholeScore dataset establishes a new benchmark to study LCOD using intraoperative video data.

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